ArticleScientific reports2021
Predicting species distributions and community composition using satellite remote sensing predictors.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
11 citing papers in PubMed, 38 citations in OpenAlex.
- Synergies Between Agricultural Production and Shorebird Conservation With Climate Change in the Central Valley, California, With Optimized Water Allocation and Multi-Benefit Land Use.Global change biology · 2025Article
- The species distribution model based on the random forest algorithm reveals the distribution patterns of Neophocaena asiaeorientalis.Scientific reports · 2025Article
- Plant diversity across dimensions: Coupling biodiversity measures from the ground and the sky.Science advances · 2025Article
- Predictions of species distributions based only on models estimating future climate change are not reliable.Scientific reports · 2024Article
- Towards an Analytical Biology.Current genomics · 2024Article
- A new threshold selection method for species distribution models with presence-only data: Extracting the mutation point of the P/E curve by threshold regression.Ecology and evolution · 2024Article
- Article
- Modeling present and future distribution of plankton populations in a coastal upwelling zone: the copepod Calanus chilensis as a study case.Scientific reports · 2023Article
- Mapping small mammal optimal habitats using satellite-derived proxy variables and species distribution models.PloS one · 2023Article
- Leaf habit affects the distribution of drought sensitivity but not water transport efficiency in the tropics.Ecology letters · 2022Article
- Integrating remote sensing with ecology and evolution to advance biodiversity conservation.Nature ecology & evolution · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models-including assemblage diversity and composition-obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.